Optimization based on modified swarm intelligence techniques for a stand-alone hybrid photovoltaic/diesel/battery system - Maleki, Akbar (Sustainable energy technologies and assessments, 2022)
•Six particle swarm intelligence algorithms have been applied for optimal configuration of a hybrid renewable energy system.•The optimization process aims to minimize the total net annual cost and is constrained to the loss of load supply probability.•Performance study of a stand-alone hybrid photovoltaic/diesel/battery system to meet the load demand of real meteorological data is carried out.•Effects of reliability index and the maximum number of iteration on optimization of hybrid systems are investigated.•PSO-III is found superior in terms of fast convergence, simulation time, and accuracy in comparison to other approaches. Stand-alone hybrid energy system (SHES) based on photovoltaic/diesel/battery, as one of the main energy resources in remote areas, is widely used for power generation. The optimal sizing and configuration of SHES is essential for achieving a cost-effective and reliable power supply. This article presents five improved particle swarm intelligence algorithms to solve the optimal configuration problem of stand-alone hybrid photovoltaic/ diesel/battery systems. The techniques include five improved particle swarm optimization (PSO) algorithms based on the modified particle velocity and particle position to improve of the local and global searches in all iterations. The objective of the optimization is to determine the optimal sizing of the SHES components (photovoltaic (PV) panels, the diesel power generator, and batteries) to compose a cost-effective and reliable scheme. The improved algorithms are compared by original PSO algorithm. The effects of reliability index, loss of load supply probability, on the optimization of the SHES have been investigated by each algorithm over 30 independent runs. The effect of the maximum number of iteration on the optimization of the hybrid system by the best algorithm for different reliability values is examined. The results show the superiority of the PSO-III in terms of fast convergence, simulation time, and accuracy in comparison to other approachs. Finally, a sensitivity analysis of the SHES shows that the value of total net annual cost (TNAC) is increased around 18% due to the decrease of reliability indexes from 10% to 2%. Also, the mean value of TNAC is decreased around 11.9% due to the increase of the number of iteration from 25 to 175.